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Amazon Kinesis Video Streams vs OneStream Live: What are the differences?
Developers describe Amazon Kinesis Video Streams as "Capture, process, and store video streams for analytics and machine learning". Amazon Kinesis Video Streams makes it easy to securely stream video from connected devices to AWS for analytics, machine learning (ML), and other processing. Kinesis Video Streams automatically provisions and elastically scales all the infrastructure needed to ingest streaming video data from millions of devices. It also durably stores, encrypts, and indexes video data in your streams, and allows you to access your data through easy-to-use APIs. On the other hand, OneStream Live is detailed as "Live Stream Pre Recorded Videos". Schedule & Live Stream Recorded Videos to 40+ Social Media Platforms Simultaneously. Multicast to 40+ streaming networks including Facebook Live, YouTube, Twitter's Periscope, Twitch, Mixer, Smashcast & many more.
Amazon Kinesis Video Streams and OneStream Live can be categorized as "Video Streaming" tools.
Some of the features offered by Amazon Kinesis Video Streams are:
- Device SDKs to securely stream data from devices
- Durable storage
- APIs for real-time and batch data retrieval
On the other hand, OneStream Live provides the following key features:
- Manage your own team members to collaborate and live stream more efficiently
- Upload videos stored in your computer, use camera to record yourself or capture your screen to live stream later
- Stream stored videos directly from Google Drive, Dropbox & OneDrive and save your internet bandwidth.
We would like to connect a number of (about 25) video streams, from an Amazon S3 bucket containing video data to endpoints accessible to a Docker image, which, when run, will process the input video streams and emit some JSON statistics.
The 25 video streams should be synchronized. Could people share their experiences with a similar scenario and perhaps offer advice about which is better (Wowza, Amazon Kinesis Video Streams) for this kind of problem, or why they chose one technology over the other?
The video stream duration will be quite long (about 8 hours each x 25 camera sources). The 25 video streams will have no audio component. If you worked with a similar problem, what was your experience with scaling, latency, resource requirements, config, etc.?
I have different experience with processing video files that I'll describe below. It might be helpful or at least make you think a bit diffferent about the problem. What I did (part of it is a mistake): To increase the level of parallelism at the time consuming step which was the video upload, using a custom cmd tool written in Python, I splitted the input videos to much smaller chunks (without losing their ordering - just file name labeling with timestamp) . It then uploaded the chunks to S3. That triggered a few Lambdas that each first pulled a chunked video, did the processing with ffmpeg (the Lambdas were the mistake - at that time the local Lambda storage was up to 512MB so lots of chunks and lots of Lambdas had to be in place, also Lambda are hell to debug), later called Rekognition and later using AWS Elemental MediaConvert to rebuild the full length video. I would use some sort of ECS deployment where processing is triggered by S3 event, and scale the number of Fargate nodes dependent on the number of chucks/videos. Then each processor pulls its video (not stream) to its local storage (local EBS drive) and works. I failed to understand why are you trying to stream videos that are basically static, as a file, or that putting the files on S3 is a current limitation (while your input videos are 'live' and streaming) that you're trying to remove ?
